A Novel Approach for Occluded Ear Recognition Based on Shape Context

Abstract

The amount of digitized application is growing fast and continuously. As the result of such growth, professional, reliable and secure techniques for identifying people inside both real and virtual worlds are mandatory. In this paper, we present a fully automatic ear-based biometric system which needs no human intervention and can be used in a real-time manner. The proposed system aims to recognize people based on their ear shape extracted from a profile facial image which usually suffers from partial occlusion caused by hair and/or earrings. First, a cascaded classifier-based ear detection approach that uses Haar-like features is used to detect ears in profile images. Later, the process is followed by a novel ear recognition technique based on Shape Context descriptor. The results of testing the proposed approach on some of the standard datasets show promising results; for non-occluded images 100% recognition achieved while for the images where the ear was occluded by both hair and earring, the accuracy was 57%.